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Design and application of interval type-2 fuzzy neural network systems optimized with hybrid algorithms

INFORMATION SCIENCES 2025年 689卷

作者： Chen, Yang Liaoning Univ Technol Coll Sci Jinzhou 121000 Peoples R China

Interval type-2 fuzzy neural network systems (IT2 FNNSs) own the advantages of IT2 fuzzy logic systems (FLSs) and NN. The paper designs a kind of IT2 FNNSs for permanent magnetic drive (PMD) forecasting. For each rules of IT2 FNNSs, whose antecedents, consequents and input measurements are chosen as Gaussian IT2 membership functions (MFs). The proposed hybrid backpropagation (BP) algorithms and recursive least square (RLS) algorithms are adopted to tune all the parameters simultaneously. Simulation instances on the basis of data of permanent magnetic drive (PMD) torque and revolutions per minute (rpm) are adopted for testing the performances of proposed hybrid optimized IT2 FNNSs for forecasting. Convergence analysis illustrates that the IT2 FNNSs have excellent generalization capability compared with both singleton and non-singleton T1 FNNSs.

关键词： Type-2 fuzzy neural network systems Interval type-2 membership functions back propagation algorithms Recursive least square algorithms Convergence analysis

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Artificial intelligence without restriction surpassing human intelligence with probability one: Theoretical insight into secrets of the brain with AI twins of the brain

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NEUROCOMPUTING 2025年 619卷

作者： Huang, Guang-Bin Westover, M. Brandon Tan, Eng-King Wang, Haibo Cui, Dongshun Ma, Wei-Ying Wang, Tiantong He, Qi Wei, Haikun Wang, Ning Tian, Qiyuan Lam, Kwok-Yan Yao, Xin Wong, Tien Yin Southeast Univ Sch Automat Nanjing Peoples R China Minist Educ Key Lab Measurement & Control Complex Syst Engn Nanjing Peoples R China Mind PointEye Singapore Singapore Harvard Med Sch Beth Israel Deaconess Med Ctr Boston MA USA Natl Neurosci Inst Dept Neurol Singapore Singapore Natl Univ Singapore Duke NUS Med Sch Singapore Singapore Sun Yat Sen Univ Affiliated Hosp 1 Res Ctr Big Data & Artificial Intelligence Med Guangzhou Peoples R China Tsinghua Univ Inst AI Ind Res Beijing Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore Tsinghua Univ Sch Biomed Engn Beijing Peoples R China Singapore Inst Technol Singapore Singapore Lingnan Univ Sch Data Sci Hong Kong Peoples R China Univ Birmingham Sch Comp Sci Birmingham England Singapore Natl Eye Ctr Singapore Eye Res Inst Singapore Singapore Tsinghua Univ Beijing Peoples R China

Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to surpass human intelligence in the future? This paper shows that in theory new AI twins with fresh cellular level of AI techniques for neuroscience could approximate the brain and its functioning systems (e.g. perception and cognition functions) with any expected small error and AI without restrictions could surpass human intelligence with probability one in the end. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of AI, especially in the realm of artificial neural networks. This paper also gives the answer to the two widely discussed fundamental questions: 1) whether AI could have potentials of discovering new principles in nature;2) whether error backpropagation (BP) algorithm commonly and efficiently used in tuning parameters in AI applications is also adopted in the brain. Intelligence is just one of fortuitous but sophisticated creations of the nature which has not been fully discovered. Like mathematics and physics, with no restrictions artificial intelligence would lead to a new subject with its selfcontained systems and principles. We anticipate that this paper opens new doors for 1) AI twins and other AI techniques to be used in cellular level of efficient neuroscience dynamic analysis, functioning analysis of the brain and brain illness solutions;2) new worldwide collaborative scheme for interdisciplinary teams concurrently working on and modelling different types of neurons and synapses and different level of functioning subsystems of the brain with AI techniques;3) development of low energy of AI techniques with the aid of fundamenta

关键词： AI Twins Neuroscience Artificial Intelligence back propagation algorithms Spiking Extreme Learning Machines Cellular AI

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Application of back propagation Neural Network algorithms on Modeling Failure of B-737 Bleed Air System Valves in Desert Conditions

Application of Back Propagation Neural Network Algorithms on...

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AEROTECH 4 - Conference on Recent Advances in Aerospace Technologies

作者： Abdelrahman, Wael G. Al-Garni, Ahmed Z. Al-Wadiee, Waheed King Fahd Univ Petr & Minerals Dept Aerosp Engn Dhahran 31261 Saudi Arabia Aviat Dept Saudi Aramco Dhahran Saudi Arabia

ISBN: (纸本)9783037855065

Accurate life prediction of aircraft engine components is very critical because it has a direct impact on aircraft safety and on operators' profits. The engine bleed air system valves have considerably high failure rates when the engines are operated in desert conditions because of sand particles erosion and blockage. In this work, an Artificial Neural Network (ANN) model for the prediction of failure rate of the most important of these valves in Boeing 737 engines is developed and validated. A previously developed feed-forward back-propagation algorithm is implemented to train the ANN. The effects of changing the number of neurons in the input layer, the number of neurons in the hidden layer, the rate of learning, and the momentum constant are investigated. The model results are validated using comparisons with actual valves failure data from a local operator in Saudi Arabia, as well as comparisons with classical Weibull model results.

关键词： reliability neural network back propagation algorithms engine valves desert conditions

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Design of back propagation optimized Nagar-Bardini structure-based interval type-2 fuzzy logic systems for fuzzy identification

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2021年第12期43卷 2780-2787页

作者： Chen, Yang Yang, Jiaxiu Liaoning Univ Technol Coll Sci Shiying Dist Jinzhou 121001 Liaoning Peoples R China

In recent years, fuzzy identification based on system identification theory has become a hot academic topic. Interval type-2 fuzzy logic systems (IT2 FLSs) have become a rising technology. This paper designs a type of Nagar-Bardini (NB) structure-based singleton IT2 FLSs for fuzzy identification problems. The antecedents of primary membership functions of IT2 FLSs are chosen as Gaussian type-2 primary membership functions with uncertain standard deviations. Then, the back propagation algorithms are used to tune the parameters of IT2 FLSs according to the chain rule of derivation. Compared with the type-1 fuzzy logic systems, simulation studies show that the proposed IT2 FLSs can obtain better abilities of generalization for fuzzy identification problems.

关键词： Fuzzy identification interval type-2 fuzzy logic systems Nagar-Bardini structure back propagation algorithms simulations

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Potential applications of artificial neural networks to thermodynamics: vapor-liquid equilibrium predictions

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COMPUTERS & CHEMICAL ENGINEERING 1999年第3期23卷 385-390页

作者： Sharma, R Singhal, D Ghosh, R Dwivedi, A Malaviya Reg Engn Coll Dept Chem Engn Jaipur 302017 Rajasthan India

The associative property of artificial neural networks (ANNs) and their inherent ability to "learn" and "recognize" highly non-linear and complex relationships finds them ideally suited to a wide range of applications in chemical engineering. Dynamic Modeling and Control of Chemical Process Systems and Fault Diagnosis are the two significant applications of ANNs that have been explored so far with success. This paper deals with the potential applications of ANNs in thermodynamics - particularly, the prediction:estimation of vapor-liquid equilibrium (VLE) data. The prediction of VLE data by conventional thermodynamic methods is tedious and requires determination of "constants" which is arbitrary in many ways. Also, the use of conventional thermodynamics for predicting VLE data for highly polar substances introduces a large number of inaccuracies. The possibility of applying ANNs for VLE data prediction/estimation has been explored using the back propagation algorithm. The methane-ethane and ammonia-water systems have been studied and the VLE predictions have been found to be accurate to within +/- 1%. Preliminary results confirm exciting possibilities of ANNs for applications to thermodynamics of mixtures. Advantages and limitations of this application are also discussed. An heuristic approach to reduce the trial and error process for selecting the "optimum" net architecture is discussed. (C) 1999 Elsevier Science Ltd. All rights reserved.

关键词： Neural networks Thermodynamics Phase equilibria Forecasting Estimation algorithms Methane Ethane Ammonia Water Heuristic methods Conformal mapping Equations of state Vapor liquid equilibrium predictions back propagation algorithms Highly polar systems

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Design of Takagi Sugeno Kang Type Interval Type-2 Fuzzy Logic Systems Optimized with Hybrid algorithms

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2023年第2期25卷 868-879页

作者： Chen, Yang Yang, Jiaxiu Li, Chenxi Liaoning Univ Technol Coll Sci Jinzhou 121001 Peoples R China

The paper designs a type of Takagi-Sugeno-Kang (TSK) type interval type-2 fuzzy logic systems for permanent magnetic drive (PMD) coercivity and maximum energy product (MEP) forecasting. The antecedents and input measurements of interval type-2 fuzzy logic systems (IT2 FLSs) are selected as Gaussian IT2 membership function (MFs) with uncertain standard deviations. The back propagation (BP) algorithms are adopted for tuning the parameters of antecedent and input measurement. Meanwhile, the recursive least square (RLS) algorithms are adopted for tuning the parameters of consequent. Monte Carlo computer simulation examples are provided to illustrate the effective of hybrid optimized IT2 FLSs in contrast to two types of type-1 (T1) FLSs.

关键词： Interval type-2 fuzzy logic systems Recursive least square algorithms back propagation algorithms Convergence Monte Carlo simulation

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Age and Gender Classification Using backpropagation and Bagging algorithms

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Computers, Materials & Continua 2023年第2期74卷 3045-3062页

作者： Ammar Almomani Mohammed Alweshah Waleed Alomoush Mohammad Alauthman Aseel Jabai Anwar Abbass Ghufran Hamad Meral Abdalla Brij B.Gupta Research and Innovation Department Skyline University CollegeP.O.Box 1797SharjahUAE IT-department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan Prince Abdullah Ben Ghazi Faculty of Information and Communication Technology Al-Balqa Applied UniversityAl-SaltJordan School of Information Technology Skyline University CollegeSharjah P.O.Box 1797United Arab Emirates Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan Department of Computer Science and Information Engineering Asia UniversityTaizhongTaiwan Department of Computer Science King Abdulaziz UniversityJeddahSaudi Arabia

Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security *** main aim of the research is to develop a system able to predicate and classify gender,age,and ***,a newsystem calledClassifyingVoice Gender,Age,and Accent(CVGAA)is *** and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender *** has high precision compared to other algorithms used in this problem,as the adaptive backpropagation algorithm had an accuracy of 98%and the Bagging algorithm had an accuracy of 98.10%in the gender identification *** has the best accuracy among all algorithms,with 55.39%accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.

关键词： Classify voice gender accent age bagging algorithms back propagation algorithms AI classifiers

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Synthetic retrieval of hourly net ecosystem exchange using the neural network model with combined MI and GOCI geostationary sensor datasets and ground-based measurements

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2017年第23期38卷 7441-7456页

作者： Yeom, Jong-Min Deo, Ravinesh Chun, Junghwa Hong, Jinkyu Kim, Dong-Su Han, Kyung-Soo Cho, Jaeil Korea Aerosp Res Inst Natl Satellite Operat & Applicat Ctr Cal Val & Data Qual Control Team Daejeon South Korea Univ Southern Queensland Inst Agr & Environm Sch Agr Computat & Environm Sci Darling Heights Qld Australia Natl Inst Forest Sci Dept Forest Conservat Daejeon South Korea Yonsei Univ Dept Atmospher Sci Seoul South Korea Korea Meteorol Adm Natl Climate Data Ctr Seogwipo South Korea Pukyong Natl Univ Dept Spatial Informat Engn Busan South Korea Chonnam Natl Univ Dept Appl Plant Sci Gwangju South Korea

Net ecosystem carbon dioxide (CO2) exchange (NEE) is a key parameter for understanding the terrestrial plant ecosystems, but it is difficult to monitor or predict over large areas at fine temporal resolutions. In this research, we estimated the hourly NEE using a combination of the integrated neural network (NN) model with geostationary satellite imagery to overcome the limitations of existing daily polar orbiting satellite-derived carbon flux products. Two sets of satellite imageries (i.e. the meteorological imager (MI) and geostationary ocean colour imager (GOCI) aboard communication, ocean, and meteorological satellite (COMS)) and CO2 flux data derived from eddy covariance measurements were used to verify the feasibility of applying hourly geostationary satellite imagery with an NN-based approach for estimating NEE at high temporal resolutions. For the NN model, the optimum neuronal architecture was established using an NN with one hidden layer that was trained using the Levenberg-Marquardt back propagation algorithm. The hourly NEE values estimated in test period from the NN model using the combined COMS MI and GOCI imagery and ground measurements as model inputs were compared with the eddy covariance NEE values from the measurement tower, which yielded reliable statistical agreement. The hourly NEE results from the NN model based on COMS MI and GOCI imagery and ground measurement data had the highest accuracy (RMSE = 2.026 mu mol m(-2) s(-2), R = 0.975), while the root mean square error (RMSE) and the regression coefficient (R) generated by the NN model based on satellite imagery as the sole input variable were relatively lower (RMSE = 3.230 mu mol m(-2) s(-2), R = 0.952). Although the simulations for the satellite-only NEE were showed as lower accuracy than the NN model that included all input variables, the hourly variations in NEE also appeared to describe its daily growth and development pattern well, indicating the possibility of deriving hourly-based prod

关键词： Satellite data back propagation algorithms Carbon dioxide Ecosystems Satellite imagery Eddy covariance Regression coefficients Terrestrial environments Oceans Computer simulation Vortices Feasibility studies Meteorological satellites Neural networks Root-mean-square errors Covariance Architecture Communication satellites Satellites Communication Mathematical models Measurement Ocean colour Imagery Hourly Accuracy Feasibility Data Satellite sensing

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Raman spectroscopy for rapid and inexpensive diagnosis of echinococcosis using the adaptive iteratively reweighted penalized least squares-Kennard-stone-back propagation neural network

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LASER PHYSICS LETTERS 2018年第8期15卷 085702-085702页

作者： Zheng, Xiangxiang Lu, Guodong Du, Guoli Yue, Xiaxia Lu, Xiaoyi Tang, Jun Mo, Jiaqing Xinjiang Univ Coll Informat Sci & Engn Urumqi 830046 Peoples R China Xinjiang Med Univ Affiliated Hosp 1 Urumqi 830000 Peoples R China Xinjiang Univ Phys & Chem Detecting Ctr Urumqi 830046 Peoples R China

A rapid and inexpensive method of screening and diagnosis for echinococcosis is proposed for Raman spectroscopy, together with improved neural networks. We use the adaptive iteratively reweighted penalized least squares (airPLS) algorithm to deduct the fluorescence background from the Raman spectra of healthy people and echinococcosis patients. The processed data was compressed into the principal component by the PLS method, and the Kennard-stone (KS) algorithm was used to divide it into a training set and a testing set. Finally, the data was put into the back propagation (BP) neural network for modeling and prediction. The results show that the true positive rate of echinococcosis diagnosis is (94.2857 +/- 4.0721)%, the true negative rate is (95.2381 +/- 0)% and the overall accuracy rate is (94.6939 +/- 2.3269)%. The algorithm is compared with three other algorithms and it is shown that its superiority can be proved. The Raman spectroscopy combined with the airPLS-KS-BP algorithm can achieve fast and accurate diagnosis of echinococcosis.

关键词： Raman spectroscopy back propagation algorithms data processing diseases fluorescence

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Automated Seizure Detection Using Multilayer Feed Forward Network Trained Using Scaled Conjugate Gradient Method

Automated Seizure Detection Using Multilayer Feed Forward Ne...

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International Conference on Current Trends in Engineering and Technology (ICCTET)

作者： Sivasankari, K. Thanushkodi, K. Kalaivanan, K. Akshaya Coll Engn & Technol Dept ECE Coimbatore Tamil Nadu India Akshaya Coll Engn & Technol Coimbatore Tamil Nadu India

ISBN: (纸本)9781479925834

Electroencephalogram (EEG) is a tool used in the diagnosis of a common neurological disorder Epilepsy. Analysis of long recordings of EEG by visual inspection for epilepsy is quite a tedious process. In this paper, we present an approach for automated epileptic seizure detection by employing Multi layer Perceptron Neural Network (MLPNN) classifier. Independent Component Analysis (ICA), a statistical tool is used for extraction of features. The ascertained signals are trained under supervision by making use of memory efficient and fast Scaled Conjugate Gradient (SCG) backpropagation algorithm. The data set is taken from a publicly available EEG database. The MLPNN is designed with the tan-sigmoid transfer function in the hidden layer and output layer. The network is analyzed using performance metric like Mean Square Error and confusion matrix. The best classification accuracy is about 100% for the overall dataset. This indicates the proposed method has potential in designing a new intelligent EEG-based assistance diagnosis system for early detection of the electroencephalographic changes.

关键词： Epilepsy Independent Component Analysis back propagation algorithms Scaled Conjugate Gradient (SCG) Epoch Regression

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